Statistical Inference Attack Against PHY-layer Key Extraction and Countermeasures
Rui Zhu, Tao Shu, and Huirong Fu

TL;DR
This paper analyzes statistical inference attacks on PHY-layer key extraction using machine learning without assuming link correlation, demonstrating effective key recovery and proposing a countermeasure that enhances randomness and security.
Contribution
It introduces ML-based inference attacks that do not rely on link correlation assumptions and proposes a new countermeasure, FBCH, to improve key extraction security.
Findings
ML algorithms effectively infer link signatures without correlation assumptions
Inference algorithms significantly reduce key search space
FBCH protocol enhances randomness and resists inference attacks
Abstract
The formal theoretical analysis on channel correlations in both real indoor and outdoor environments are provided in this paper. Moreover, this paper studies empirical statistical inference attacks (SIA) against LSB key extraction, whereby an adversary infers the signature of a target link. Consequently, the secret key extracted from that signature has been recovered by observing the surrounding links. Prior work assumes theoretical link-correlation models for the inference, in contrast, our study does not make any assumption on link correlation. Instead, we take machine learning (ML) methods for link inference based on empirically measured link signatures. ML algorithms have been developed to launch SIAs under various realistic scenarios. Our experimental results have shown that the proposed inference algorithms are still quite effective even without making assumptions on link…
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Taxonomy
TopicsWireless Communication Security Techniques · Wireless Signal Modulation Classification · Biometric Identification and Security
